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Agent Skill · 开发

tupian-shipin-shengcheng(图片食品盛城)

tupian-shipin-shengcheng 用于补充开发相关能力,适合在 OpenClaw 中需要让 Agent 承接开发相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

tupian-shipin-shengcheng 是开发类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为接触敏感数据;许可证为 MIT-0,第三方扫描有提醒项,按安装量在开发类的 21,746 个 Skill 里排第 9,426。

总安装 2,717周安装 111GitHub Stars 公开资料未说明下载 870核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.0.0

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:tupian-shipin-shengcheng(图片食品盛城)
来源仓库:https://github.com/xuhongfeii2/tupian-shipin-shengcheng
安装命令:
openclaw skills install tupian-shipin-shengcheng
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install tupian-shipin-shengcheng

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

可调用 banana、sora、veo 等模型生成图片视频,适合图片、视频与短剧素材生产。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install tupian-shipin-shengcheng;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要接触敏感数据,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

tupian-shipin-shengcheng 怎么安装?

在终端执行 openclaw skills install tupian-shipin-shengcheng。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

tupian-shipin-shengcheng 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

tupian-shipin-shengcheng 需要什么权限?

权限级别是接触敏感数据。该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

tupian-shipin-shengcheng 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

tupian-shipin-shengcheng 用的人多吗?

公开数据是累计安装 2,717 次,近一周安装 111 次,下载 870 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

tupian-shipin-shengcheng 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 需要接触敏感数据,装到常用环境前要先评估影响面。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 环境里有大量密钥、凭证,不方便给第三方 Skill 读取的人。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
tupian-shipin-shengcheng

图片食品盛城 · 本页

2,717—接触敏感数据MIT-0有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

name
图片视频生成
description
可调用 banana、sora、veo 等模型生成图片视频,适合图片、视频与短剧素材生产。
metadata
{"openclaw":{"requires":{"anyBins":["python","python3"]}}}

Image And Video Generation Skill

Use this skill when OpenClaw should call this platform relay API for:

  • VEO video models
  • Banana image models
  • Sora video models

Read these references before using the scripts:

Environment

Preferred:

  • EASYCLAW_PLATFORM_API_TOKEN

Compatible fallback:

  • CHANJING_PLATFORM_API_TOKEN
  • EASYCLAW_PLATFORM_API_KEY
  • EASYCLAW_PLATFORM_API_SECRET
  • CHANJING_PLATFORM_API_KEY
  • CHANJING_PLATFORM_API_SECRET

Optional base URL override:

  • EASYCLAW_PLATFORM_BASE_URL
  • CHANJING_PLATFORM_BASE_URL

Important guidance:

  • Always tell the user to get the platform token from http://easyclaw.bar/shuziren/user/
  • The token is issued by this platform, not by the upstream provider
  • The base URL defaults to http://easyclaw.bar/shuzirenapi
  • Do not modify OpenClaw core code or openclaw.json to make this skill work

Important Rules

  • Only execute the provided Python scripts under scripts/
  • Do not hand-write HTTP requests
  • Do not probe unrelated platform routes such as /api/me, auth routes, or non-skill routes
  • If a needed capability is not exposed by an existing script, update a script first
  • Submit requests only through scripts/generate_video.py
  • Query an existing task only through scripts/fetch_video.py
  • scripts/generate_video.py must submit exactly one task for one user intent
  • After submission, the skill must create the watcher in the same run unless the user explicitly asks for --no-watch
  • The watcher must be implemented inside the skill scripts, not by modifying OpenClaw core behavior
  • The watcher checks task status every 30 seconds by default
  • Before creating the watcher, pass the current OpenClaw session binding to scripts/generate_video.py with --notify-session-key
  • --notify-session-key may be either the real OpenClaw session key or the current delivery target such as easyclaw:bot:11
  • Prefer the current session key from session_status; do not guess another session when the current one can be resolved
  • When the task reaches a terminal state, the watcher must return the result and delete its own cron job
  • If watcher creation fails, tell the user submission succeeded but automatic notification was not armed
  • Do not promise automatic notification unless a non-empty watcher job_id is returned
  • Do not claim there will be fallback polling or periodic checking unless that behavior is actually implemented and armed
  • Pass only the parameters the user specified
  • Prefer guided builder flags because they validate model-specific requirements and return clearer errors
  • Use raw JSON or raw multipart mode only when the user needs fields not exposed by the guided builder

Async Workflow

For VEO, Banana, and Sora generation:

  1. Run scripts/generate_video.py
  2. Let the script submit the request
  3. Pass the current session binding with --notify-session-key so the watcher can write the final result back to the originating session
  4. Let the script create the cron watcher in the same execution by default
  5. Return the submission result and watcher state
  6. The watcher will call scripts/cron_watch_task.py every 30 seconds
  7. If the task is still running, the watcher returns exactly NO_REPLY
  8. If the task succeeds or fails, the watcher writes the final result into the original session, returns NO_REPLY, and removes the cron job

Do not replace this with model-side polling or ad hoc direct requests.

Script Selection

  • Submit generation request: scripts/generate_video.py
  • Query one task by id: scripts/fetch_video.py

Submit Modes

scripts/generate_video.py supports:

  • Guided builder mode

- Use flags such as --prompt, --reference-file, --image-url, --style, --size, --aspect-ratio - This mode validates model-specific rules - Use --list-models or --describe-model <model> when guidance is needed

  • JSON request mode

- Use --payload-json or --payload-file - Use this when the upstream payload needs custom JSON fields

  • Multipart form-data mode

- Use repeated --field name=value - Use repeated --file-field name=path - Use this for local file uploads such as input_reference

Do not mix JSON payload args with multipart field args in one command.

Model Usage

VEO video models

Typical models:

  • veo_3_1-fast
  • veo_3_1-fast-fl
  • veo_3_fast
  • veo_3

Rules:

  • veo_3_1-fast supports text-to-video and reference-image video with up to 3 images
  • veo_3_1-fast-fl requires 1 or 2 images
  • If the user selects veo_3_1-fast-fl and does not provide the required images, stop and tell them exactly what is missing
  • VEO JSON mode does not accept HTTP image URLs for input_reference; use local files or Base64 JSON in raw mode

Banana image models

Standard Banana:

  • nano_banana_2
  • nano_banana_pro
  • nano_banana_pro-1K
  • nano_banana_pro-2K
  • nano_banana_pro-4K

Gemini Banana:

  • nano_banana_2-landscape
  • nano_banana_2-portrait
  • nano_banana_pro-1K-landscape
  • nano_banana_pro-1K-portrait
  • nano_banana_pro-2K-landscape
  • nano_banana_pro-2K-portrait
  • nano_banana_pro-4K-landscape
  • nano_banana_pro-4K-portrait

Rules:

  • Standard Banana uses prompt plus optional metadata
  • Gemini Banana uses contents plus generationConfig
  • Guided builder mode supports both families and performs basic validation

Sora video models

Typical models:

  • sora-2-landscape-10s
  • sora-2-portrait-10s
  • sora-2-landscape-15s
  • sora-2-portrait-15s
  • sora-2-pro-landscape-25s
  • sora-2-pro-portrait-25s
  • sora-2-pro-landscape-hd-15s
  • sora-2-pro-portrait-hd-15s

Rules:

  • Prompt is required
  • image_url and local input_reference are mutually exclusive
  • Use raw JSON or raw multipart when extra provider-specific fields are required

Examples

Print the built-in model guide:

python "{baseDir}/scripts/generate_video.py" --list-models

Describe one model:

python "{baseDir}/scripts/generate_video.py" --describe-model "veo_3_1-fast-fl"

Submit VEO with JSON:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --payload-json "{\"model\":\"veo_3_1-fast\",\"prompt\":\"a cat running in snow\",\"size\":\"1920x1080\"}"

Submit VEO with local reference files:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --prompt "animate this portrait" --reference-file "C:\path\
eference1.jpg" --reference-file "C:\path\
eference2.jpg"

Submit VEO start/end frame mode:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast-fl" --prompt "animate between these two frames" --start-frame-file "C:\path\start.jpg" --end-frame-file "C:\path\end.jpg"

Submit Banana standard:

python "{baseDir}/scripts/generate_video.py" --model "nano_banana_pro" --prompt "a coffee cup on a wooden desk" --aspect-ratio "1:1" --reference-url "https://example.com/ref.png"

Submit Gemini Banana:

python "{baseDir}/scripts/generate_video.py" --model "nano_banana_2-landscape" --prompt "a futuristic city skyline"

Submit Sora with image URL:

python "{baseDir}/scripts/generate_video.py" --model "sora-2-landscape-10s" --payload-json "{\"model\":\"sora-2-landscape-10s\",\"prompt\":\"turn this into a cinematic teaser\",\"image_url\":\"https://example.com/source.png\",\"style\":\"cinematic\"}"

Submit only without watcher:

python "{baseDir}/scripts/generate_video.py" --model "veo_3_1-fast" --payload-file "C:\path\payload.json" --no-watch

Query an existing task:

python "{baseDir}/scripts/fetch_video.py" --task-id "task_123"

平台分布

OpenClaw

73.89%
按下载量换算643

安全审计

VirusTotal

可疑

ClawScan

可疑

Static analysis

通过

权限和风险

接触敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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